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2021,30(10):1-11, DOI: 10.15888/j.cnki.csa.008126
Abstract:
The diversity of crowd scale in reality is a great challenge to crowd counting algorithms. Therefore, a novel crowd counting algorithm based on scale fusion is proposed in this study. Firstly, the algorithm for density map generation is optimized. Multiple head detectors are used to obtain part of the head scales of the sparse crowd, and RBF interpolation is employed to complete this part of the density map. As to the dense part of crowd, the traditional distance self-adaptive algorithm is adopted to generate a more accurate density map. Secondly, the regression neural network of the density map is designed with a mobile inverted bottleneck convolution module, and a dilated convolution module is added to facilitate the extraction of head edge features. Finally, the loss function of the regression neural network is optimized by distinguishing the crowd area from the non-crowd area. In the experiment part, the algorithm is compared with other similar algorithms on multiple datasets, and the results show that the proposed method can significantly improve the accuracy of crowd counting.
The diversity of crowd scale in reality is a great challenge to crowd counting algorithms. Therefore, a novel crowd counting algorithm based on scale fusion is proposed in this study. Firstly, the algorithm for density map generation is optimized. Multiple head detectors are used to obtain part of the head scales of the sparse crowd, and RBF interpolation is employed to complete this part of the density map. As to the dense part of crowd, the traditional distance self-adaptive algorithm is adopted to generate a more accurate density map. Secondly, the regression neural network of the density map is designed with a mobile inverted bottleneck convolution module, and a dilated convolution module is added to facilitate the extraction of head edge features. Finally, the loss function of the regression neural network is optimized by distinguishing the crowd area from the non-crowd area. In the experiment part, the algorithm is compared with other similar algorithms on multiple datasets, and the results show that the proposed method can significantly improve the accuracy of crowd counting.
2021,30(10):12-20, DOI: 10.15888/j.cnki.csa.008048
Abstract:
The device fault generally starts from a minor one and gradually develops to the loss of working capacity of the whole set. Detection in case of a minor fault can recover the unnecessary loss. Therefore, this study proposes a method to evaluate device health status on the basis of the Weighted Mahalanobis Distance (WMD) and the Device Status Index (DSI). Based on an improved Mahalanobis-Taguchi system, the method constructs a stable reference space for the characteristic parameters during the effective operation of the device. It selects the characteristics and calculates the WMD according to the device fault sensitivity, eliminating the interference of characteristic correlation. Then Box-Cox transformation is used to determine the threshold value of the DSI to build a health status model of the complex heavy device, and the model is verified by experiments. The WMD values of the normal samples are all below the fault threshold, and nearly 98.6% of the sample values are within the warning signs. The proposed method can provide data support for maintenance and management of complex heavy devices, thereby facilitating industrial production.
The device fault generally starts from a minor one and gradually develops to the loss of working capacity of the whole set. Detection in case of a minor fault can recover the unnecessary loss. Therefore, this study proposes a method to evaluate device health status on the basis of the Weighted Mahalanobis Distance (WMD) and the Device Status Index (DSI). Based on an improved Mahalanobis-Taguchi system, the method constructs a stable reference space for the characteristic parameters during the effective operation of the device. It selects the characteristics and calculates the WMD according to the device fault sensitivity, eliminating the interference of characteristic correlation. Then Box-Cox transformation is used to determine the threshold value of the DSI to build a health status model of the complex heavy device, and the model is verified by experiments. The WMD values of the normal samples are all below the fault threshold, and nearly 98.6% of the sample values are within the warning signs. The proposed method can provide data support for maintenance and management of complex heavy devices, thereby facilitating industrial production.
2021,30(10):21-30, DOI: 10.15888/j.cnki.csa.008137
Abstract:
As the intelligence level grows, a large amount of new knowledge is generated all the time, and knowledge graph has gradually become one of the tools for knowledge management. However, the existing knowledge graph still has some problems, such as missing attributes, sparse relations, and massive noisy information, which leads to poor graph quality and is easy to affect various tasks in the field of natural language processing. As a research hotspot, the knowledge reasoning technology oriented to the knowledge graph is the main method to solve this problem. It improves the information of the knowledge graph by simulating the human reasoning process, with a good performance in many applications. Taking the knowledge graph as the pointcut, this study classifies and explains the knowledge reasoning technology by categories and elaborates on several application tasks of the technology, such as intelligent question-answering and the recommendation system. Finally, it forecasts the main research directions in the future and puts forward several research ideas.
As the intelligence level grows, a large amount of new knowledge is generated all the time, and knowledge graph has gradually become one of the tools for knowledge management. However, the existing knowledge graph still has some problems, such as missing attributes, sparse relations, and massive noisy information, which leads to poor graph quality and is easy to affect various tasks in the field of natural language processing. As a research hotspot, the knowledge reasoning technology oriented to the knowledge graph is the main method to solve this problem. It improves the information of the knowledge graph by simulating the human reasoning process, with a good performance in many applications. Taking the knowledge graph as the pointcut, this study classifies and explains the knowledge reasoning technology by categories and elaborates on several application tasks of the technology, such as intelligent question-answering and the recommendation system. Finally, it forecasts the main research directions in the future and puts forward several research ideas.
2021,30(10):31-39, DOI: 10.15888/j.cnki.csa.008104
Abstract:
Due to the shaking of a handheld camera or the movement of targets, the video image data is subject to motion blur, which reduces the image quality of human perception. With regard to the problem, from how to obtain clear images from the original process to how to obtain clear images efficiently, a new model for real-time video image deblurring based on the lightweight Generative Adversarial Network (GAN) is proposed in this study. The model defines PatchGAN as a discriminant network and sets up a dual-scale discriminator for global images and local features on the basis of it; the generation network takes lightweight MobileNetV3 as the backbone network and introduces a feature pyramid for feature extraction to solve the problem of low utilization of feature information in the discrimination network and low inference efficiency of the generation network. This model uses an end-to-end approach to efficiently deblur the video image. After experiments on the GoPro and Kohler datasets, the results show that the sharp image deblurred by this model has a high peak signal-to-noise ratio and great structural similarity, and the inference speed reaches 1.7–127 times faster than that of other models.
Due to the shaking of a handheld camera or the movement of targets, the video image data is subject to motion blur, which reduces the image quality of human perception. With regard to the problem, from how to obtain clear images from the original process to how to obtain clear images efficiently, a new model for real-time video image deblurring based on the lightweight Generative Adversarial Network (GAN) is proposed in this study. The model defines PatchGAN as a discriminant network and sets up a dual-scale discriminator for global images and local features on the basis of it; the generation network takes lightweight MobileNetV3 as the backbone network and introduces a feature pyramid for feature extraction to solve the problem of low utilization of feature information in the discrimination network and low inference efficiency of the generation network. This model uses an end-to-end approach to efficiently deblur the video image. After experiments on the GoPro and Kohler datasets, the results show that the sharp image deblurred by this model has a high peak signal-to-noise ratio and great structural similarity, and the inference speed reaches 1.7–127 times faster than that of other models.
2021,30(10):40-47, DOI: 10.15888/j.cnki.csa.008161
Abstract:
The next Point-Of-Interest (POI) recommendation is one of the most important services of the Location-Based Social Network (LBSN). It can not only help users find the destination which they are interested in, but also improve the potential income of business providers. Existing algorithms have employed user behavior sequences and the POI information for recommendation, but none of them fully utilize POI side information, thereby failing to ease the problems of cold start and sparse data. In light of the above analysis, this study proposed a POI recommendation system, Graph Embedding-Gated Recurrent Unit (GE-GRU). Firstly, GE-GRU relies on Graph Embedding (GE) to integrate the POI itself with its side information to get the POI embedding that contains deep information. Then, the POI embedding is input into the GRU-based neural network to model recent user preferences to acquire user embedding. Finally, according to the POI rank list, the next POI can be recommended. Experiments are conducted on a real dataset, Foursquare, which contains more than 480 000 check-ins, and Accuracy@k is adopted for evaluation. The results show that, compared with GRU and Long Short-Term Memory (LSTM), GE-GRU has 3% and 7% improvement on Accuracy@10, respectively.
The next Point-Of-Interest (POI) recommendation is one of the most important services of the Location-Based Social Network (LBSN). It can not only help users find the destination which they are interested in, but also improve the potential income of business providers. Existing algorithms have employed user behavior sequences and the POI information for recommendation, but none of them fully utilize POI side information, thereby failing to ease the problems of cold start and sparse data. In light of the above analysis, this study proposed a POI recommendation system, Graph Embedding-Gated Recurrent Unit (GE-GRU). Firstly, GE-GRU relies on Graph Embedding (GE) to integrate the POI itself with its side information to get the POI embedding that contains deep information. Then, the POI embedding is input into the GRU-based neural network to model recent user preferences to acquire user embedding. Finally, according to the POI rank list, the next POI can be recommended. Experiments are conducted on a real dataset, Foursquare, which contains more than 480 000 check-ins, and Accuracy@k is adopted for evaluation. The results show that, compared with GRU and Long Short-Term Memory (LSTM), GE-GRU has 3% and 7% improvement on Accuracy@10, respectively.
2021,30(10):48-58, DOI: 10.15888/j.cnki.csa.008106
Abstract:
Streamline rendering has long remained as one of the most common techniques for flow visualization. The streamline is an effective sparse representation of the flow field, which can capture the flow behavior, but generating streamline needs long-term particle tracing and massive integral operations. Large-scale flow visualization takes considerable computation time, and the parallel computing algorithm and high-performance equipment are needed. In this study, a high-resolution streamline generation algorithm based on deep learning is designed. The initial sparse low-resolution streamline is quickly mapped into the dense high-resolution streamline to provide reliable streamline visualization results in a short time. On this basis, an interactive real-time flow visualization system is developed, which is capable of flow-field feature detection, attribute correlation analysis, information theory analysis, etc. It can help users quickly understand the flow field data and find their areas of interest for post-hoc analysis, avoiding redundant data and enhancing work efficiency. In addition, it can meet the users’ needs for multi-dimensional correlation analysis of flow field structures, features, and attributes.
Streamline rendering has long remained as one of the most common techniques for flow visualization. The streamline is an effective sparse representation of the flow field, which can capture the flow behavior, but generating streamline needs long-term particle tracing and massive integral operations. Large-scale flow visualization takes considerable computation time, and the parallel computing algorithm and high-performance equipment are needed. In this study, a high-resolution streamline generation algorithm based on deep learning is designed. The initial sparse low-resolution streamline is quickly mapped into the dense high-resolution streamline to provide reliable streamline visualization results in a short time. On this basis, an interactive real-time flow visualization system is developed, which is capable of flow-field feature detection, attribute correlation analysis, information theory analysis, etc. It can help users quickly understand the flow field data and find their areas of interest for post-hoc analysis, avoiding redundant data and enhancing work efficiency. In addition, it can meet the users’ needs for multi-dimensional correlation analysis of flow field structures, features, and attributes.
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Available online: July 13, 2021 , DOI: 10.15888/j.cnki.csa.008280
Abstract:
With the continuous development of digital twin technology at this stage, research and applications surrounding digital twins have gradually become a hot spot. Because traditional automated driving test methods have various defects in terms of functionality, safety, and test cost, this article proposes a digital twin automatic driving test method based on the basic characteristics of the digital twin and the test method of autonomous driving. The method of constructing the driving test environment uses spatial coordinate mapping, collision detection model, and virtual scene registration to map the automatic driving information in the actual environment to the virtual scene. At the same time, the corresponding mixed reality automatic driving test model is constructed and passed the experiment. The collision test with interactive features of the mixed reality system is shown. The performance of the system at sampling frequencies of 50ms, 200ms and 1000ms is compared and analyzed. Experiments show that the algorithm in this paper has better operating frame rate characteristics at the sampling frequency of 200ms or above.
With the continuous development of digital twin technology at this stage, research and applications surrounding digital twins have gradually become a hot spot. Because traditional automated driving test methods have various defects in terms of functionality, safety, and test cost, this article proposes a digital twin automatic driving test method based on the basic characteristics of the digital twin and the test method of autonomous driving. The method of constructing the driving test environment uses spatial coordinate mapping, collision detection model, and virtual scene registration to map the automatic driving information in the actual environment to the virtual scene. At the same time, the corresponding mixed reality automatic driving test model is constructed and passed the experiment. The collision test with interactive features of the mixed reality system is shown. The performance of the system at sampling frequencies of 50ms, 200ms and 1000ms is compared and analyzed. Experiments show that the algorithm in this paper has better operating frame rate characteristics at the sampling frequency of 200ms or above.
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Abstract:
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
Abstract:
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
Abstract:
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
Abstract:
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
Abstract:
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
Abstract:
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
Abstract:
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
Abstract:
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
Abstract:
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
Abstract:
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
Abstract:
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
Abstract:
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
Abstract:
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
Abstract:
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
Abstract:
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
Abstract:
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
Abstract:
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Abstract:
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
Abstract:
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
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Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address：4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code：100190
Phone：010-62661041 Fax： Email：csa (a) iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
